Anti-saturation prescribed-time control for stochastic systems of free-flying space robots using a self-adapting non-monotonic approach

This paper proposes an anti-saturation prescribed-time control scheme for free-flying space robots (FFSRs) subject to system uncertainties, external disturbances, input saturation, and output constraint. Initially, the control scheme is developed based on a newly constructed stochastic model, introducing stochastic neural networks (SNNs) to approximate the lumped stochastic factor that encompasses system uncertainties and external disturbances. Subsequently, a novel self-adapting non-monotonic prescribed-time function is proposed, integrating input saturation as an adaptive variable to dynamically adjust the constraint boundaries. This integration enables the constraint boundaries to adaptively expand in a non-monotonic manner in response to the occurrence of input saturation. Moreover, the constraint boundaries are designed as tunnel-shaped to prevent overshoot. The proposed control scheme ensures that all closed-loop signals are semi-globally uniformly ultimately bounded in probability, with the tracking error stabilized within a prescribed time. Finally, simulation results validate the effectiveness and superiority of the proposed scheme.